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Background: Endometrial cancer is the most common gynaecological cancer in the western world, the incidence increasing in the United Kingdom by over 40% since 1993. Two types of endometrial cancer exist - oestrogen-dependent type 1 with good prognosis and non-oestrogen-dependent type 2 with poor prognosis. The histopathological distribution of the increase in endometrial cancer is unknown. This study investigates the observed incidence trends of the two types, the age, stage, and socioeconomic distribution of this increase and survival outcome.Methods: Data were analysed from 6867 women with endometrial cancer registered between 1994 and 2006, at a UK population-based cancer registry.Results: Increased endometrial cancer incidence is confined to type 1 cancers with a significant increase in age standardised incidence rate (ASR) from 12.0 per 100,000 (confidence interval (CI) 10.7-13.2) in 1994 to 16.3 per 100,000 (CI 14.9-17.7), P<0.001 in 2006, while ASR of type 2 cancer changed from 2.5 per 100,000 (CI 2.0-3.1) in 1994 to 2.2 per 100,000 (CI 1.7-2.7) in 2006, which was not statistically significant P>0.05. Increase in type 1 cancer is most marked in age groups 60-69 years (P<0.001) and 70-79 years (P<0.001) and distributed equally among socioeconomic quintiles. While outcome for type 1 cancer has improved, 1-year survival in type 2 cancer is unchanged from 73.1% in 1994 to 74.3%, P=0.089 and 5-year survival decreased from 55.1% to 40.9%, P=0.001.Conclusion: Increased incidence in endometrial cancer is confined to type 1 cancers, seen most in the 60-79 age groups and across all socioeconomic quintiles. Survival in type 2 cancer has decreased significantly. Urgent research is needed to investigate prevention strategies in type 1 and improve therapy in type 2 cancers. [ABSTRACT FROM AUTHOR]

Frequentist confidence intervals for population ranks and their statistical justifications are not well established, even though there is a great need for such procedures in practice. How do we assign confidence bounds for the ranks of health care facilities, schools, and financial institutions based on data that do not clearly separate the performance of different entities apart? The commonly used bootstrap-based frequentist confidence intervals and Bayesian intervals for population ranks may not achieve the intended coverage probability in the frequentist sense, especially in the presence of unknown ties or "near ties" among the populations to be ranked. Given random samples from k populations, we propose confidence bounds for population ranking parameters and develop rigorous frequentist theory and nonstandard bootstrap inference for population ranks, which allow ties and near ties. In the process, a notion of modified population rank is introduced that appears quite suitable for dealing with the population ranking problem. The proposed methodology and theoretical results are illustrated through simulations and a real dataset from a health research study involving 79 Veterans Health Administration (VHA) facilities. The results are extended to general risk adjustment models. [ABSTRACT FROM AUTHOR]

Intervalles de confiance à rétrécisseur pour l'espérance d'une loi normale: utilisation d'une valeur initiale au profit d'une meilleure efficacité Si l'espérance inconnue d'une population univariée est assez proche d'une valeur initiale, l'erreur quadratique moyenne d'un bon estimateur à rétrécisseur sera plus faible que celle de la moyenne échantillonnale. Ce principe est connu depuis longtemps, mais il ne semble pas avoir été appliqué à l'estimation par intervalle. L'auteur présente des intervalles de confiance 'à rétrécisseur' bilatéraux à 95% et 99% pour l'espérance d'une loi normale. Ces intervalles sont plus courts que l'intervalle habituel fondé sur la loi de Student dès que l'espérance de la population se situe dans un 'intervalle effectif.' On peut observer une réduction de 20% de la longueur moyenne de l'intervalle quand l'espérance de la population est assez proche de la valeur initiale. L'auteur montre aussi comment certains estimateurs ponctuels à rétrécisseur d'une espérance univariée quelconque peuvent être modifiés de façon à élargir l'intervalle effectif. [ABSTRACT FROM AUTHOR]

The annual percent change (APC) is often used to measure trends in disease and mortality rates, and a common estimator of this parameter uses a linear model on the log of the age-standardized rates. Under the assumption of linearity on the log scale, which is equivalent to a constant change assumption, APC can be equivalently defined in three ways as transformations of either (1) the slope of the line that runs through the log of each rate, (2) the ratio of the last rate to the first rate in the series, or (3) the geometric mean of the proportional changes in the rates over the series. When the constant change assumption fails then the first definition cannot be applied as is, while the second and third definitions unambiguously define the same parameter regardless of whether the assumption holds. We call this parameter the percent change annualized (PCA) and propose two new estimators of it. The first, the two-point estimator, uses only the first and last rates, assuming nothing about the rates in between. This estimator requires fewer assumptions and is asymptotically unbiased as the size of the population gets large, but has more variability since it uses no information from the middle rates. The second estimator is an adaptive one and equals the linear model estimator with a high probability when the rates are not significantly different from linear on the log scale, but includes fewer points if there are significant departures from that linearity. For the two-point estimator we can use confidence intervals previously developed for ratios of directly standardized rates. For the adaptive estimator, we show through simulation that the bootstrap confidence intervals give appropriate coverage. [ABSTRACT FROM AUTHOR]

Let pi[sub N] be a finite population of size N whose elements have distinct X values X[sub (1)] < ... < X[sub (N)]. Let chi ([sub 1]) < ... < x(sub (n)] denote the order statistics of a simple random sample of size n taken from pi [sub N] without replacement. An outer confidence interval is formed for the quantile interval [X ([sub t]), X [sub (u)] (1

A capture-recapture model that depends on the "catchability" of the individuals in a closed population is considered. A test is derived to decide if the capture efficiencies are the same for two capture attempts. The test given assumes no a priori information about the population and can be used even if one is dealing with relatively small populations. Statistical inferences are drawn about the size of the population. In particular, a family of point estimators for the population size, N, is investigated and compared with the maximum likelihood estimator. Further, confidence intervals for the capture efficiency, is an element of, are combined with joint confidence regions for N and is an element of to obtain confidence intervals for N. It is found that this procedure yields confidence intervals for N whose actual confidence levels are not strongly dependent on the nuisance parameter is an element of. For N less than or equal to 200 the procedure given for finding confidence intervals for N is found to be more satisfactory than the standard approach of simply replacing is an element of with its maximum-likelihood estimate. [ABSTRACT FROM AUTHOR]

Explicit expressions for the level of significance and power of Stein's two-stage hypothesis testing procedure are derived when the normal population is mixed with another normal population differing in the mean, Approximate expansions for these expressions have been obtained, and numerical studies of the size have been carried out using the expansions. Some numerical indication of the accuracy of the approximations has also been obtained, Stein's two-stage procedure is quite robust against mixtures of normal populations differing in location parameters. Similar conclusions can be drawn regarding the coverage probabilities of confidence intervals generated by this procedure. [ABSTRACT FROM AUTHOR]

Suppose mu[sub i], sigma[sup 2, sub i] are the mean and variance of the ith normal population, i = 1, 2, 3 (we assume the parameters to be all unknown). For given nonzero constants lambda[sub 1], lambda[sub 2], lambda[sub 3], suppose mu= SIGMA[sup 3, sub i]=1 lambda[sub i] mu[sub i]. Sequential procedures have been derived (i) to arrive at a fixed width confidence interval for mu, and (ii) to estimate mu pointwise with minimum risk (loss being squared error). Some desirable asymptotic properties are proved for the stopping times in both cases. Monte Carlo studies were made for the problem in (i) with moderate optimal sample sizes. [ABSTRACT FROM AUTHOR]

Meta-analysis is a quantitative method for summarizing single studies. In a meta-analysis, one tries to quantify the treatment effect, assigning more weight to large studies than to small studies. A much used method for weighting is the inverse variance method. If all studies have measured the results in the same way, the results can be used directly in the meta-analysis, but if the same outcome is measured in different ways across different studies, one has to use a standardized effect size where results are converted to a common scale. If it is believed that the effect is consistent across various populations and settings, one can employ a fixed-effect model. If systematic differences in effect can be expected, a random-effects model is used. Meta-analyses are often depicted as forest plots. Each line represents one study where the effect estimate is marked as a point on a line, with each end of the line representing the confidence interval around it. The meta-analysis is shown as a diamond where the width illustrates the uncertainty around the estimate. If all study results point in the same direction, the meta-analysis is considered "homogeneous". But if the studies vary in their effect size and direction, the findings are "heterogeneous". The strength of meta-analysis is that it can be used to summarize a large body of information in one number. This is also its limitation. One number cannot describe the variation that exists across different studies. [ABSTRACT FROM AUTHOR]

This article presents information on statistical calculations carried out to estimate the largest mean. A single-sample procedure is proposed for obtaining asymptotically optimum confidence intervals for the largest mean of normal populations with common unknown variance. In this article a single-sample procedure is considered for this situation of common unknown variance. The procedure is analogous to the well known procedure based on the distribution for the one sample problem and utilizes a pooled estimate of the common population variance.